{
 "metadata": {
  "name": "",
  "signature": "sha256:ce622ec60e1428608a712c0ebe9241c4492db6eb09bc48804e615a9d525ac15d"
 },
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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import pandas as pd\n",
      "from os.path import join\n",
      "\n",
      "from pylab import rcParams\n",
      "import matplotlib.pyplot as plt\n",
      "%matplotlib inline\n",
      "rcParams['figure.figsize'] = (16, 8)\n",
      "\n",
      "import nilmtk\n",
      "from nilmtk import DataSet, TimeFrame, MeterGroup, HDFDataStore\n",
      "from nilmtk.disaggregate import CombinatorialOptimisation\n",
      "from nilmtk.utils import print_dict\n",
      "from nilmtk.metrics import f1_score\n",
      "\n",
      "import warnings\n",
      "warnings.filterwarnings(\"ignore\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Demo of NILMTK v0.2 for BuildSys 2014"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Downloading data"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The full data set can be downloaded from the remote WikiEnergy database. The credentials are omitted here for security reasons."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# download_wikienergy(database_username, database_password, hdf_filename)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Loading data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data_dir = '/Users/nipunbatra/Dropbox/nilmtk_datasets/'\n",
      "we = DataSet(join(data_dir, 'redd.h5'))\n",
      "print('loaded ' + str(len(we.buildings)) + ' buildings')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "loaded 6 buildings\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Examine dataset metadata"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print_dict(we.metadata)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<ul><li><strong>meter_devices</strong>: <ul><li><strong>eMonitor</strong>: <ul><li><strong>max_sample_period</strong>: 50</li><li><strong>description</strong>: <a href=\"Measures circuit-level power demand.  Comes with 24 CTs. This FAQ page suggests the eMonitor measures real (active) power: http://www.energycircle.com/node/14103  although the REDD readme.txt says all channels record apparent power.\n",
        "\">Measures circuit-level power demand.  Comes with 24 CTs. This FAQ page suggests the eMonitor measures real (active) power: http://www.energycircle.com/node/14103  although the REDD readme.txt says all channels record apparent power.\n",
        "</a></li><li><strong>manufacturer_url</strong>: <a href=\"http://powerhousedynamics.com\">http://powerhousedynamics.com</a></li><li><strong>measurements</strong>: <ul><li>{'type': 'active', 'upper_limit': 5000, 'lower_limit': 0, 'physical_quantity': 'power'}</li></ul></li><li><strong>sample_period</strong>: 3</li><li><strong>wireless</strong>: False</li><li><strong>model</strong>: eMonitor</li><li><strong>manufacturer</strong>: Powerhouse Dynamics</li></ul></li><li><strong>REDD_whole_house</strong>: <ul><li><strong>max_sample_period</strong>: 30</li><li><strong>wireless</strong>: False</li><li><strong>measurements</strong>: <ul><li>{'type': 'apparent', 'upper_limit': 50000, 'lower_limit': 0, 'physical_quantity': 'power'}</li></ul></li><li><strong>description</strong>: </li><li><strong>sample_period</strong>: 1</li></ul></li></ul></li><li><strong>creators</strong>: <ul><li>Kolter, Zico</li><li>Johnson, Matthew</li></ul></li><li><strong>name</strong>: REDD</li><li><strong>description</strong>: Several weeks of power data for 6 different homes.</li><li><strong>number_of_buildings</strong>: 6</li><li><strong>related_documents</strong>: <ul><li><a href=\"http://redd.csail.mit.edu\">http://redd.csail.mit.edu</a></li><li><a href=\"J. Zico Kolter and Matthew J. Johnson. REDD: A public data set for energy disaggregation research. In proceedings of the SustKDD workshop on Data Mining Applications in Sustainability, 2011. http://redd.csail.mit.edu/kolter-kddsust11.pdf\n",
        "\">J. Zico Kolter and Matthew J. Johnson. REDD: A public data set for energy disaggregation research. In proceedings of the SustKDD workshop on Data Mining Applications in Sustainability, 2011. http://redd.csail.mit.edu/kolter-kddsust11.pdf\n",
        "</a></li></ul></li><li><strong>long_name</strong>: The Reference Energy Disaggregation Data set</li><li><strong>contact</strong>: zkolter@cs.cmu.edu</li><li><strong>geo_location</strong>: <ul><li><strong>latitude</strong>: 42.360091</li><li><strong>country</strong>: US</li><li><strong>longitude</strong>: -71.09416</li><li><strong>locality</strong>: Massachusetts</li></ul></li><li><strong>publication_date</strong>: 2011</li><li><strong>timezone</strong>: US/Eastern</li><li><strong>schema</strong>: <a href=\"https://github.com/nilmtk/nilm_metadata/tree/v0.2\">https://github.com/nilmtk/nilm_metadata/tree/v0.2</a></li><li><strong>institution</strong>: Massachusetts Institute of Technology (MIT)</li><li><strong>subject</strong>: Disaggregated power demand from domestic buildings.</li></ul>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x10ab36d10>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Examine metadata for a single house"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "building_number = 1\n",
      "print_dict(we.buildings[building_number].metadata)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<ul><li><strong>instance</strong>: 1</li><li><strong>dataset</strong>: REDD</li><li><strong>original_name</strong>: house_1</li></ul>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x10ab369d0>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Examine sub-metered appliances"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "elec = we.buildings[building_number].elec\n",
      "elec.appliances"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "[Appliance(type='fridge', instance=1),\n",
        " Appliance(type='washer dryer', instance=1),\n",
        " Appliance(type='dish washer', instance=1),\n",
        " Appliance(type='light', instance=1),\n",
        " Appliance(type='electric space heater', instance=1),\n",
        " Appliance(type='electric stove', instance=1),\n",
        " Appliance(type='electric oven', instance=1),\n",
        " Appliance(type='light', instance=3),\n",
        " Appliance(type='sockets', instance=1),\n",
        " Appliance(type='microwave', instance=1),\n",
        " Appliance(type='sockets', instance=2),\n",
        " Appliance(type='sockets', instance=3),\n",
        " Appliance(type='sockets', instance=4),\n",
        " Appliance(type='light', instance=2),\n",
        " Appliance(type='unknown', instance=2),\n",
        " Appliance(type='unknown', instance=1)]"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Wiring hierarchy of meters"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "elec.meters[1].when_on().next().head(100)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "2011-04-18 09:22:09-04:00    True\n",
        "2011-04-18 09:22:10-04:00    True\n",
        "2011-04-18 09:22:11-04:00    True\n",
        "2011-04-18 09:22:12-04:00    True\n",
        "2011-04-18 09:22:13-04:00    True\n",
        "2011-04-18 09:22:14-04:00    True\n",
        "2011-04-18 09:22:15-04:00    True\n",
        "2011-04-18 09:22:16-04:00    True\n",
        "2011-04-18 09:22:17-04:00    True\n",
        "2011-04-18 09:22:18-04:00    True\n",
        "2011-04-18 09:22:19-04:00    True\n",
        "2011-04-18 09:22:20-04:00    True\n",
        "2011-04-18 09:22:21-04:00    True\n",
        "2011-04-18 09:22:22-04:00    True\n",
        "2011-04-18 09:22:23-04:00    True\n",
        "...\n",
        "2011-04-18 09:23:34-04:00    True\n",
        "2011-04-18 09:23:35-04:00    True\n",
        "2011-04-18 09:23:36-04:00    True\n",
        "2011-04-18 09:23:37-04:00    True\n",
        "2011-04-18 09:23:38-04:00    True\n",
        "2011-04-18 09:23:39-04:00    True\n",
        "2011-04-18 09:23:40-04:00    True\n",
        "2011-04-18 09:23:41-04:00    True\n",
        "2011-04-18 09:23:42-04:00    True\n",
        "2011-04-18 09:23:43-04:00    True\n",
        "2011-04-18 09:23:44-04:00    True\n",
        "2011-04-18 09:23:45-04:00    True\n",
        "2011-04-18 09:23:46-04:00    True\n",
        "2011-04-18 09:23:47-04:00    True\n",
        "2011-04-18 09:23:48-04:00    True\n",
        "Name: (power, apparent), Length: 100"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "elec.draw_wiring_graph()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "(<networkx.classes.digraph.DiGraph at 0x10439da90>,\n",
        " <matplotlib.axes._axes.Axes at 0x10ac50490>)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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jQ/369Tl+/PjD/hQiIiIiOrVQRERE5N9q48aNBAQEMGzYMKKiopgwYQLvvvsusbGxBAUF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\nYakJa6B8AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10ab36b50>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Select all fridges"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fridges = nilmtk.global_meter_group.select_using_appliances(type='fridge')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Proportion of energy per fridge"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The energy consumed by each appliance can be expressed as a proportion of the household's total energy.  Here we find the range of proportions for each fridge."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Select a subset of fridges, otherwise the computation takes a long time\n",
      "fridges_restricted = MeterGroup(fridges.meters[:5])\n",
      "\n",
      "proportion_per_fridge = fridges_restricted.proportion_of_upstream_total_per_meter()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "Calculating total_energy for ElecMeterID(instance=1, building=1, dataset='REDD') ...   "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "Calculating total_energy for ElecMeterID(instance=2, building=1, dataset='REDD') ...   "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1/5 ElecMeter(instance=5, building=1, dataset='REDD', appliances=[Appliance(type='fridge', instance=1)]) = nan"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "Calculating total_energy for ElecMeterID(instance=1, building=2, dataset='REDD') ...   "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "Calculating total_energy for ElecMeterID(instance=2, building=2, dataset='REDD') ...   "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "2/5 ElecMeter(instance=9, building=2, dataset='REDD', appliances=[Appliance(type='fridge', instance=1)]) = nan"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "Calculating total_energy for ElecMeterID(instance=1, building=3, dataset='REDD') ...   "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "Calculating total_energy for ElecMeterID(instance=2, building=3, dataset='REDD') ...   "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "3/5 ElecMeter(instance=7, building=3, dataset='REDD', appliances=[Appliance(type='fridge', instance=1)]) = nan"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "Calculating total_energy for ElecMeterID(instance=1, building=5, dataset='REDD') ...   "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "Calculating total_energy for ElecMeterID(instance=2, building=5, dataset='REDD') ...   "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "4/5 ElecMeter(instance=18, building=5, dataset='REDD', appliances=[Appliance(type='fridge', instance=1)]) = nan"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "Calculating total_energy for ElecMeterID(instance=1, building=6, dataset='REDD') ...   "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "Calculating total_energy for ElecMeterID(instance=2, building=6, dataset='REDD') ...   "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "5/5 ElecMeter(instance=8, building=6, dataset='REDD', appliances=[Appliance(type='fridge', instance=1)]) = nan"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "proportion_per_fridge.plot(kind='bar');\n",
      "plt.title('fridge energy as proportion of total building energy');\n",
      "plt.ylabel('proportion');\n",
      "plt.xlabel('Fridge (<appliance instance>, <building instance>, <dataset name>)');"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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vzOKauz8/Z0FLbp7FZZIOHvL/H6ZLRaWqvimL6wW/I4vD8wHWxpxnY75lPvsU/Pfcxja3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\nzMzMzMys67iza2ZmZmZmZl3n/wHaDCtPCBj+FAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x111cae110>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# How much energy does the largest-consuming fridge consume in kWh?\n",
      "fridges.select(building=61).total_energy()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fridges.select(building=61).plot();"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x10bc8dbd0>"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Daily energy consumption across fridges in WikiEnergy data set"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fridges_restricted = MeterGroup(fridges.meters[:20])\n",
      "daily_energy = pd.DataFrame([meter.average_energy_per_period(offset_alias='D') \n",
      "                             for meter in fridges_restricted.meters])\n",
      "\n",
      "daily_energy.plot(kind='hist');\n",
      "plt.title('Histogram of daily fridge energy');\n",
      "plt.xlabel('energy (kWh)');\n",
      "plt.ylabel('occurences');\n",
      "plt.legend().set_visible(False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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JWmunWmsPOvhxPoajR8PSA7BnWHoARvpQfZFHP2TRD1n0QxY9GZYegBkc+Q5t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\ngK30/wMkkGQ46fBZWAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x111950990>"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Plot sub-metered data for a single day"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "we.store.window = TimeFrame(start='2010-04-01 00:00:00-05:00', end='2014-04-03 00:00:00-05:00')\n",
      "elec.plot();"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
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ahIQERo0axbFjx1iyZAkPPPAAO3bsYPz48YwcOZInn3ySnJwcli5dSlpaGrNm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\nK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/neI78v1658t6zYeHusY1mz5h3ym98skTfe+EGs9SPkgkCclSvl6+vXy3c2b67rveXw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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10b1e8b90>"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Plot fraction of energy consumption of each appliance"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fraction = elec.submeters().fraction_per_meter().dropna()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1/10 ElecMeter(instance=2, building=11, dataset='WikiEnergy', appliances=[Appliance(type='air conditioner', instance=1)])"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Using cached result.\n",
        "\r",
        "2/10 ElecMeter(instance=3, building=11, dataset='WikiEnergy', appliances=[Appliance(type='sockets', instance=1)])"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Using cached result.\n",
        "\r",
        "3/10 ElecMeter(instance=4, building=11, dataset='WikiEnergy', appliances=[Appliance(type='sockets', instance=2)])"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Using cached result.\n",
        "\r",
        "4/10 ElecMeter(instance=5, building=11, dataset='WikiEnergy', appliances=[Appliance(type='dish washer', instance=1)])"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Using cached result.\n",
        "\r",
        "5/10 ElecMeter(instance=6, building=11, dataset='WikiEnergy', appliances=[Appliance(type='spin dryer', instance=1)])"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Using cached result.\n",
        "\r",
        "6/10 ElecMeter(instance=7, building=11, dataset='WikiEnergy', appliances=[Appliance(type='electric furnace', instance=1)])"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Using cached result.\n",
        "\r",
        "7/10 ElecMeter(instance=8, building=11, dataset='WikiEnergy', appliances=[Appliance(type='sockets', instance=3)])"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Using cached result.\n",
        "\r",
        "8/10 ElecMeter(instance=9, building=11, dataset='WikiEnergy', appliances=[Appliance(type='sockets', instance=4)])"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Using cached result.\n",
        "\r",
        "9/10 ElecMeter(instance=10, building=11, dataset='WikiEnergy', appliances=[Appliance(type='fridge', instance=1)])"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Using cached result.\n",
        "\r",
        "10/10 ElecMeter(instance=11, building=11, dataset='WikiEnergy', appliances=[Appliance(type='electric water heating appliance', instance=1)])"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Using cached result.\n"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "elec.clear_cache()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Removed building11/elec/cache/meter1/\n",
        "Removed building11/elec/cache/meter2/\n",
        "Removed building11/elec/cache/meter3/\n",
        "Removed building11/elec/cache/meter4/\n",
        "Removed building11/elec/cache/meter5/\n",
        "Removed building11/elec/cache/meter6/\n",
        "Removed building11/elec/cache/meter7/\n",
        "Removed building11/elec/cache/meter8/\n",
        "Removed building11/elec/cache/meter9/\n",
        "Removed building11/elec/cache/meter10/\n",
        "Removed building11/elec/cache/meter11/\n"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "elec.submeters().fraction_per_meter()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1/10 ElecMeter(instance=2, building=11, dataset='WikiEnergy', appliances=[Appliance(type='air conditioner', instance=1)])"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Calculating energy for column ('power', 'active')\n",
        "\r",
        "2/10 ElecMeter(instance=3, building=11, dataset='WikiEnergy', appliances=[Appliance(type='sockets', instance=1)])"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Calculating energy for column ('power', 'active')\n",
        "\r",
        "3/10 ElecMeter(instance=4, building=11, dataset='WikiEnergy', appliances=[Appliance(type='sockets', instance=2)])"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Calculating energy for column ('power', 'active')\n",
        "\r",
        "4/10 ElecMeter(instance=5, building=11, dataset='WikiEnergy', appliances=[Appliance(type='dish washer', instance=1)])"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Calculating energy for column ('power', 'active')\n",
        "\r",
        "5/10 ElecMeter(instance=6, building=11, dataset='WikiEnergy', appliances=[Appliance(type='spin dryer', instance=1)])"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Calculating energy for column ('power', 'active')\n",
        "\r",
        "6/10 ElecMeter(instance=7, building=11, dataset='WikiEnergy', appliances=[Appliance(type='electric furnace', instance=1)])"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Calculating energy for column ('power', 'active')\n",
        "\r",
        "7/10 ElecMeter(instance=8, building=11, dataset='WikiEnergy', appliances=[Appliance(type='sockets', instance=3)])"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Calculating energy for column ('power', 'active')\n",
        "\r",
        "8/10 ElecMeter(instance=9, building=11, dataset='WikiEnergy', appliances=[Appliance(type='sockets', instance=4)])"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Calculating energy for column ('power', 'active')\n",
        "\r",
        "9/10 ElecMeter(instance=10, building=11, dataset='WikiEnergy', appliances=[Appliance(type='fridge', instance=1)])"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Calculating energy for column ('power', 'active')\n",
        "\r",
        "10/10 ElecMeter(instance=11, building=11, dataset='WikiEnergy', appliances=[Appliance(type='electric water heating appliance', instance=1)])"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Calculating energy for column ('power', 'active')\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "2                            NaN\n",
        "3                            NaN\n",
        "4                            NaN\n",
        "5                            NaN\n",
        "6                            NaN\n",
        "7                            NaN\n",
        "8                            NaN\n",
        "9                            NaN\n",
        "10                           NaN\n",
        "11                           NaN\n",
        "(2, 11, WikiEnergy)     0.541636\n",
        "(3, 11, WikiEnergy)     0.046624\n",
        "(4, 11, WikiEnergy)     0.011947\n",
        "(5, 11, WikiEnergy)     0.053094\n",
        "(6, 11, WikiEnergy)     0.007034\n",
        "(7, 11, WikiEnergy)     0.127455\n",
        "(8, 11, WikiEnergy)     0.024780\n",
        "(9, 11, WikiEnergy)     0.058938\n",
        "(10, 11, WikiEnergy)    0.121804\n",
        "(11, 11, WikiEnergy)    0.006687\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "elec.submeters().fraction_per_meter()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1/10 ElecMeter(instance=2, building=11, dataset='WikiEnergy', appliances=[Appliance(type='air conditioner', instance=1)])"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Using cached result.\n",
        "\r",
        "2/10 ElecMeter(instance=3, building=11, dataset='WikiEnergy', appliances=[Appliance(type='sockets', instance=1)])"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Using cached result.\n",
        "\r",
        "3/10 ElecMeter(instance=4, building=11, dataset='WikiEnergy', appliances=[Appliance(type='sockets', instance=2)])"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Using cached result.\n",
        "\r",
        "4/10 ElecMeter(instance=5, building=11, dataset='WikiEnergy', appliances=[Appliance(type='dish washer', instance=1)])"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Using cached result.\n",
        "\r",
        "5/10 ElecMeter(instance=6, building=11, dataset='WikiEnergy', appliances=[Appliance(type='spin dryer', instance=1)])"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Using cached result.\n",
        "\r",
        "6/10 ElecMeter(instance=7, building=11, dataset='WikiEnergy', appliances=[Appliance(type='electric furnace', instance=1)])"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Using cached result.\n",
        "\r",
        "7/10 ElecMeter(instance=8, building=11, dataset='WikiEnergy', appliances=[Appliance(type='sockets', instance=3)])"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Using cached result.\n",
        "\r",
        "8/10 ElecMeter(instance=9, building=11, dataset='WikiEnergy', appliances=[Appliance(type='sockets', instance=4)])"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Using cached result.\n",
        "\r",
        "9/10 ElecMeter(instance=10, building=11, dataset='WikiEnergy', appliances=[Appliance(type='fridge', instance=1)])"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Using cached result.\n",
        "\r",
        "10/10 ElecMeter(instance=11, building=11, dataset='WikiEnergy', appliances=[Appliance(type='electric water heating appliance', instance=1)])"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Using cached result.\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "2                            NaN\n",
        "3                            NaN\n",
        "4                            NaN\n",
        "5                            NaN\n",
        "6                            NaN\n",
        "7                            NaN\n",
        "8                            NaN\n",
        "9                            NaN\n",
        "10                           NaN\n",
        "11                           NaN\n",
        "(2, 11, WikiEnergy)     0.541636\n",
        "(3, 11, WikiEnergy)     0.046624\n",
        "(4, 11, WikiEnergy)     0.011947\n",
        "(5, 11, WikiEnergy)     0.053094\n",
        "(6, 11, WikiEnergy)     0.007034\n",
        "(7, 11, WikiEnergy)     0.127455\n",
        "(8, 11, WikiEnergy)     0.024780\n",
        "(9, 11, WikiEnergy)     0.058938\n",
        "(10, 11, WikiEnergy)    0.121804\n",
        "(11, 11, WikiEnergy)    0.006687\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Create convenient labels\n",
      "labels = elec.get_appliance_labels(fraction.index)\n",
      "plt.figure(figsize=(8,8))\n",
      "fraction.plot(kind='pie', labels=labels);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ILjTVmq26Ni1Y1sEP4TItZFqNqwKm0zJg+uCZFkyrOVx6mFbL9IAZBz8Ofqrl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\nAAAAAAAAAAAAAAAAAAAAAAAAAG5hAyiVvuY1o3NEAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fbb18f46ad0>"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Select meters on the basic of appliance category"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "elec.select_using_appliances(category='heating')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "MeterGroup(meters=\n",
        "  ElecMeter(instance=7, building=11, dataset='WikiEnergy', appliances=[Appliance(type='electric furnace', instance=1)])\n",
        "  ElecMeter(instance=11, building=11, dataset='WikiEnergy', appliances=[Appliance(type='electric water heating appliance', instance=1)])\n",
        ")"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Find all appliances with a particular type of motor\n",
      "elec.select_using_appliances(category='single-phase induction motor')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "MeterGroup(meters=\n",
        "  ElecMeter(instance=2, building=11, dataset='WikiEnergy', appliances=[Appliance(type='air conditioner', instance=1)])\n",
        "  ElecMeter(instance=5, building=11, dataset='WikiEnergy', appliances=[Appliance(type='dish washer', instance=1)])\n",
        "  ElecMeter(instance=6, building=11, dataset='WikiEnergy', appliances=[Appliance(type='spin dryer', instance=1)])\n",
        "  ElecMeter(instance=7, building=11, dataset='WikiEnergy', appliances=[Appliance(type='electric furnace', instance=1)])\n",
        "  ElecMeter(instance=10, building=11, dataset='WikiEnergy', appliances=[Appliance(type='fridge', instance=1)])\n",
        ")"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Training and disaggregation"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Train\n",
      "co = CombinatorialOptimisation()\n",
      "co.train(elec)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Training model for submeter 'ElecMeter(instance=5, building=1, dataset='REDD', appliances=[Appliance(type='fridge', instance=1)])'\n",
        "Training model for submeter 'ElecMeter(instance=6, building=1, dataset='REDD', appliances=[Appliance(type='dish washer', instance=1)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Training model for submeter 'ElecMeter(instance=7, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=1)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Training model for submeter 'ElecMeter(instance=8, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=2)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Training model for submeter 'ElecMeter(instance=9, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=1)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Training model for submeter 'ElecMeter(instance=11, building=1, dataset='REDD', appliances=[Appliance(type='microwave', instance=1)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Training model for submeter 'ElecMeter(instance=12, building=1, dataset='REDD', appliances=[Appliance(type='unknown', instance=1)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Training model for submeter 'ElecMeter(instance=13, building=1, dataset='REDD', appliances=[Appliance(type='electric space heater', instance=1)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Training model for submeter 'ElecMeter(instance=14, building=1, dataset='REDD', appliances=[Appliance(type='electric stove', instance=1)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Training model for submeter 'ElecMeter(instance=15, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=3)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Training model for submeter 'ElecMeter(instance=16, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=4)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Training model for submeter 'ElecMeter(instance=17, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=2)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Training model for submeter 'ElecMeter(instance=18, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=3)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Training model for submeter 'ElecMeter(instance=19, building=1, dataset='REDD', appliances=[Appliance(type='unknown', instance=2)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Training model for submeter 'MeterGroup(meters=\n",
        "  ElecMeter(instance=3, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
        "  ElecMeter(instance=4, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
        ")'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\r",
        "Loading data for meter ElecMeterID(instance=3, building=1, dataset='REDD')     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "Loading data for meter ElecMeterID(instance=4, building=1, dataset='REDD')     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Done loading data all meters for this chunk.\n",
        "Training model for submeter 'MeterGroup(meters=\n",
        "  ElecMeter(instance=10, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
        "  ElecMeter(instance=20, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
        ")'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\r",
        "Loading data for meter"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " ElecMeterID(instance=10, building=1, dataset='REDD')     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "Loading data for meter ElecMeterID(instance=20, building=1, dataset='REDD')     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Done loading data all meters for this chunk.\n",
        "Done training!"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for model in co.model:\n",
      "    print_dict(model)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<ul><li><strong>states</strong>: [  0 202]</li><li><strong>training_metadata</strong>: ElecMeter(instance=5, building=1, dataset='REDD', appliances=[Appliance(type='fridge', instance=1)])</li></ul>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x10b25f750>"
       ]
      },
      {
       "html": [
        "<ul><li><strong>states</strong>: [  0 618]</li><li><strong>training_metadata</strong>: ElecMeter(instance=6, building=1, dataset='REDD', appliances=[Appliance(type='dish washer', instance=1)])</li></ul>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x10b25f9d0>"
       ]
      },
      {
       "html": [
        "<ul><li><strong>states</strong>: [ 0 21]</li><li><strong>training_metadata</strong>: ElecMeter(instance=7, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=1)])</li></ul>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x10b25f110>"
       ]
      },
      {
       "html": [
        "<ul><li><strong>states</strong>: [ 0 28]</li><li><strong>training_metadata</strong>: ElecMeter(instance=8, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=2)])</li></ul>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x10b25fad0>"
       ]
      },
      {
       "html": [
        "<ul><li><strong>states</strong>: [ 0 73]</li><li><strong>training_metadata</strong>: ElecMeter(instance=9, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=1)])</li></ul>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x10b25ff50>"
       ]
      },
      {
       "html": [
        "<ul><li><strong>states</strong>: [   0 1196]</li><li><strong>training_metadata</strong>: ElecMeter(instance=11, building=1, dataset='REDD', appliances=[Appliance(type='microwave', instance=1)])</li></ul>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x10b25f990>"
       ]
      },
      {
       "html": [
        "<ul><li><strong>states</strong>: [  0 386]</li><li><strong>training_metadata</strong>: ElecMeter(instance=12, building=1, dataset='REDD', appliances=[Appliance(type='unknown', instance=1)])</li></ul>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x10b25f810>"
       ]
      },
      {
       "html": [
        "<ul><li><strong>states</strong>: [ 0 12]</li><li><strong>training_metadata</strong>: ElecMeter(instance=13, building=1, dataset='REDD', appliances=[Appliance(type='electric space heater', instance=1)])</li></ul>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x10b25f4d0>"
       ]
      },
      {
       "html": [
        "<ul><li><strong>states</strong>: [  0 916]</li><li><strong>training_metadata</strong>: ElecMeter(instance=14, building=1, dataset='REDD', appliances=[Appliance(type='electric stove', instance=1)])</li></ul>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x10b25f910>"
       ]
      },
      {
       "html": [
        "<ul><li><strong>states</strong>: [   0 1059]</li><li><strong>training_metadata</strong>: ElecMeter(instance=15, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=3)])</li></ul>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x10b25f210>"
       ]
      },
      {
       "html": [
        "<ul><li><strong>states</strong>: [   0 1518]</li><li><strong>training_metadata</strong>: ElecMeter(instance=16, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=4)])</li></ul>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x10b25fd90>"
       ]
      },
      {
       "html": [
        "<ul><li><strong>states</strong>: [ 0 65]</li><li><strong>training_metadata</strong>: ElecMeter(instance=17, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=2)])</li></ul>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x10b25f250>"
       ]
      },
      {
       "html": [
        "<ul><li><strong>states</strong>: [ 0 54]</li><li><strong>training_metadata</strong>: ElecMeter(instance=18, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=3)])</li></ul>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x10b25fc90>"
       ]
      },
      {
       "html": [
        "<ul><li><strong>states</strong>: [0]</li><li><strong>training_metadata</strong>: ElecMeter(instance=19, building=1, dataset='REDD', appliances=[Appliance(type='unknown', instance=2)])</li></ul>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x10b25fdd0>"
       ]
      },
      {
       "html": [
        "<ul><li><strong>states</strong>: [   0 1150]</li><li><strong>training_metadata</strong>: MeterGroup(meters=\n",
        "  ElecMeter(instance=3, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
        "  ElecMeter(instance=4, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
        ")</li></ul>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x10b25fe10>"
       ]
      },
      {
       "html": [
        "<ul><li><strong>states</strong>: [   0 2008]</li><li><strong>training_metadata</strong>: MeterGroup(meters=\n",
        "  ElecMeter(instance=10, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
        "  ElecMeter(instance=20, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
        ")</li></ul>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x10b25fe50>"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Disaggregate\n",
      "disag_filename = join(data_dir, 'redd-disag.h5')\n",
      "output = HDFDataStore(disag_filename, 'w')\n",
      "co.disaggregate(elec.mains(), output)\n",
      "output.close()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "Loading data for meter ElecMeterID(instance=1, building=1, dataset='REDD')     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "Loading data for meter ElecMeterID(instance=2, building=1, dataset='REDD')     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Done loading data all meters for this chunk.\n",
        "vampire_power = 90.8099975586 watts"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\r",
        "Loading data for meter"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " ElecMeterID(instance=1, building=1, dataset='REDD')     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "Loading data for meter ElecMeterID(instance=2, building=1, dataset='REDD')     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Done loading data all meters for this chunk.\n",
        "Estimating power demand for 'ElecMeter(instance=5, building=1, dataset='REDD', appliances=[Appliance(type='fridge', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=6, building=1, dataset='REDD', appliances=[Appliance(type='dish washer', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=7, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=8, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=2)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=9, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=11, building=1, dataset='REDD', appliances=[Appliance(type='microwave', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=12, building=1, dataset='REDD', appliances=[Appliance(type='unknown', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=13, building=1, dataset='REDD', appliances=[Appliance(type='electric space heater', instance=1)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=14, building=1, dataset='REDD', appliances=[Appliance(type='electric stove', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=15, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=3)])'\n",
        "Estimating power demand for 'ElecMeter(instance=16, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=4)])'\n",
        "Estimating power demand for 'ElecMeter(instance=17, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=2)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=18, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=3)])'\n",
        "Estimating power demand for 'ElecMeter(instance=19, building=1, dataset='REDD', appliances=[Appliance(type='unknown', instance=2)])'\n",
        "Estimating power demand for 'MeterGroup(meters=\n",
        "  ElecMeter(instance=3, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
        "  ElecMeter(instance=4, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
        ")'\n",
        "Estimating power demand for 'MeterGroup(meters=\n",
        "  ElecMeter(instance=10, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
        "  ElecMeter(instance=20, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
        ")'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\r",
        "Loading data for meter ElecMeterID(instance=1, building=1, dataset='REDD')     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "Loading data for meter ElecMeterID(instance=2, building=1, dataset='REDD')     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Done loading data all meters for this chunk.\n",
        "Estimating power demand for 'ElecMeter(instance=5, building=1, dataset='REDD', appliances=[Appliance(type='fridge', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=6, building=1, dataset='REDD', appliances=[Appliance(type='dish washer', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=7, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=8, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=2)])'\n",
        "Estimating power demand for 'ElecMeter(instance=9, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=1)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=11, building=1, dataset='REDD', appliances=[Appliance(type='microwave', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=12, building=1, dataset='REDD', appliances=[Appliance(type='unknown', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=13, building=1, dataset='REDD', appliances=[Appliance(type='electric space heater', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=14, building=1, dataset='REDD', appliances=[Appliance(type='electric stove', instance=1)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=15, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=3)])'\n",
        "Estimating power demand for 'ElecMeter(instance=16, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=4)])'\n",
        "Estimating power demand for 'ElecMeter(instance=17, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=2)])'\n",
        "Estimating power demand for 'ElecMeter(instance=18, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=3)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=19, building=1, dataset='REDD', appliances=[Appliance(type='unknown', instance=2)])'\n",
        "Estimating power demand for 'MeterGroup(meters=\n",
        "  ElecMeter(instance=3, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
        "  ElecMeter(instance=4, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
        ")'\n",
        "Estimating power demand for 'MeterGroup(meters=\n",
        "  ElecMeter(instance=10, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
        "  ElecMeter(instance=20, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
        ")'\n",
        "\r",
        "Loading data for meter"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " ElecMeterID(instance=1, building=1, dataset='REDD')     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "Loading data for meter ElecMeterID(instance=2, building=1, dataset='REDD')     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Done loading data all meters for this chunk.\n",
        "Estimating power demand for 'ElecMeter(instance=5, building=1, dataset='REDD', appliances=[Appliance(type='fridge', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=6, building=1, dataset='REDD', appliances=[Appliance(type='dish washer', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=7, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=8, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=2)])'\n",
        "Estimating power demand for 'ElecMeter(instance=9, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=11, building=1, dataset='REDD', appliances=[Appliance(type='microwave', instance=1)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=12, building=1, dataset='REDD', appliances=[Appliance(type='unknown', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=13, building=1, dataset='REDD', appliances=[Appliance(type='electric space heater', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=14, building=1, dataset='REDD', appliances=[Appliance(type='electric stove', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=15, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=3)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=16, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=4)])'\n",
        "Estimating power demand for 'ElecMeter(instance=17, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=2)])'\n",
        "Estimating power demand for 'ElecMeter(instance=18, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=3)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=19, building=1, dataset='REDD', appliances=[Appliance(type='unknown', instance=2)])'\n",
        "Estimating power demand for 'MeterGroup(meters=\n",
        "  ElecMeter(instance=3, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
        "  ElecMeter(instance=4, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
        ")'\n",
        "Estimating power demand for 'MeterGroup(meters=\n",
        "  ElecMeter(instance=10, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
        "  ElecMeter(instance=20, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
        ")'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\r",
        "Loading data for meter ElecMeterID(instance=1, building=1, dataset='REDD')     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "Loading data for meter ElecMeterID(instance=2, building=1, dataset='REDD')     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Done loading data all meters for this chunk.\n",
        "Estimating power demand for 'ElecMeter(instance=5, building=1, dataset='REDD', appliances=[Appliance(type='fridge', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=6, building=1, dataset='REDD', appliances=[Appliance(type='dish washer', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=7, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=8, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=2)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=9, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=11, building=1, dataset='REDD', appliances=[Appliance(type='microwave', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=12, building=1, dataset='REDD', appliances=[Appliance(type='unknown', instance=1)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=13, building=1, dataset='REDD', appliances=[Appliance(type='electric space heater', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=14, building=1, dataset='REDD', appliances=[Appliance(type='electric stove', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=15, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=3)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=16, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=4)])'\n",
        "Estimating power demand for 'ElecMeter(instance=17, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=2)])'\n",
        "Estimating power demand for 'ElecMeter(instance=18, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=3)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=19, building=1, dataset='REDD', appliances=[Appliance(type='unknown', instance=2)])'\n",
        "Estimating power demand for 'MeterGroup(meters=\n",
        "  ElecMeter(instance=3, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
        "  ElecMeter(instance=4, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
        ")'\n",
        "Estimating power demand for 'MeterGroup(meters=\n",
        "  ElecMeter(instance=10, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
        "  ElecMeter(instance=20, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
        ")'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\r",
        "Loading data for meter ElecMeterID(instance=1, building=1, dataset='REDD')     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "Loading data for meter ElecMeterID(instance=2, building=1, dataset='REDD')     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Done loading data all meters for this chunk.\n",
        "\r",
        "Loading data for meter ElecMeterID(instance=1, building=1, dataset='REDD')     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "Loading data for meter ElecMeterID(instance=2, building=1, dataset='REDD')     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Done loading data all meters for this chunk.\n",
        "Estimating power demand for 'ElecMeter(instance=5, building=1, dataset='REDD', appliances=[Appliance(type='fridge', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=6, building=1, dataset='REDD', appliances=[Appliance(type='dish washer', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=7, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=8, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=2)])'\n",
        "Estimating power demand for 'ElecMeter(instance=9, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=1)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=11, building=1, dataset='REDD', appliances=[Appliance(type='microwave', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=12, building=1, dataset='REDD', appliances=[Appliance(type='unknown', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=13, building=1, dataset='REDD', appliances=[Appliance(type='electric space heater', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=14, building=1, dataset='REDD', appliances=[Appliance(type='electric stove', instance=1)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=15, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=3)])'\n",
        "Estimating power demand for 'ElecMeter(instance=16, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=4)])'\n",
        "Estimating power demand for 'ElecMeter(instance=17, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=2)])'\n",
        "Estimating power demand for 'ElecMeter(instance=18, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=3)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=19, building=1, dataset='REDD', appliances=[Appliance(type='unknown', instance=2)])'\n",
        "Estimating power demand for 'MeterGroup(meters=\n",
        "  ElecMeter(instance=3, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
        "  ElecMeter(instance=4, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
        ")'\n",
        "Estimating power demand for 'MeterGroup(meters=\n",
        "  ElecMeter(instance=10, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
        "  ElecMeter(instance=20, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
        ")'\n",
        "\r",
        "Loading data for meter"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " ElecMeterID(instance=1, building=1, dataset='REDD')     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "Loading data for meter ElecMeterID(instance=2, building=1, dataset='REDD')     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Done loading data all meters for this chunk.\n",
        "Estimating power demand for 'ElecMeter(instance=5, building=1, dataset='REDD', appliances=[Appliance(type='fridge', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=6, building=1, dataset='REDD', appliances=[Appliance(type='dish washer', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=7, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=8, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=2)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=9, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=11, building=1, dataset='REDD', appliances=[Appliance(type='microwave', instance=1)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=12, building=1, dataset='REDD', appliances=[Appliance(type='unknown', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=13, building=1, dataset='REDD', appliances=[Appliance(type='electric space heater', instance=1)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=14, building=1, dataset='REDD', appliances=[Appliance(type='electric stove', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=15, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=3)])'\n",
        "Estimating power demand for 'ElecMeter(instance=16, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=4)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=17, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=2)])'\n",
        "Estimating power demand for 'ElecMeter(instance=18, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=3)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=19, building=1, dataset='REDD', appliances=[Appliance(type='unknown', instance=2)])'\n",
        "Estimating power demand for 'MeterGroup(meters=\n",
        "  ElecMeter(instance=3, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
        "  ElecMeter(instance=4, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
        ")'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'MeterGroup(meters=\n",
        "  ElecMeter(instance=10, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
        "  ElecMeter(instance=20, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
        ")'\n",
        "\r",
        "Loading data for meter"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " ElecMeterID(instance=1, building=1, dataset='REDD')     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "Loading data for meter ElecMeterID(instance=2, building=1, dataset='REDD')     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Done loading data all meters for this chunk.\n",
        "Estimating power demand for 'ElecMeter(instance=5, building=1, dataset='REDD', appliances=[Appliance(type='fridge', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=6, building=1, dataset='REDD', appliances=[Appliance(type='dish washer', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=7, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=8, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=2)])'\n",
        "Estimating power demand for 'ElecMeter(instance=9, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=1)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=11, building=1, dataset='REDD', appliances=[Appliance(type='microwave', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=12, building=1, dataset='REDD', appliances=[Appliance(type='unknown', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=13, building=1, dataset='REDD', appliances=[Appliance(type='electric space heater', instance=1)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=14, building=1, dataset='REDD', appliances=[Appliance(type='electric stove', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=15, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=3)])'\n",
        "Estimating power demand for 'ElecMeter(instance=16, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=4)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=17, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=2)])'\n",
        "Estimating power demand for 'ElecMeter(instance=18, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=3)])'\n",
        "Estimating power demand for 'ElecMeter(instance=19, building=1, dataset='REDD', appliances=[Appliance(type='unknown', instance=2)])'\n",
        "Estimating power demand for 'MeterGroup(meters=\n",
        "  ElecMeter(instance=3, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
        "  ElecMeter(instance=4, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
        ")'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'MeterGroup(meters=\n",
        "  ElecMeter(instance=10, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
        "  ElecMeter(instance=20, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
        ")'\n",
        "\r",
        "Loading data for meter"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " ElecMeterID(instance=1, building=1, dataset='REDD')     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "Loading data for meter ElecMeterID(instance=2, building=1, dataset='REDD')     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Done loading data all meters for this chunk.\n",
        "Estimating power demand for 'ElecMeter(instance=5, building=1, dataset='REDD', appliances=[Appliance(type='fridge', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=6, building=1, dataset='REDD', appliances=[Appliance(type='dish washer', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=7, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=1)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=8, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=2)])'\n",
        "Estimating power demand for 'ElecMeter(instance=9, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=1)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=11, building=1, dataset='REDD', appliances=[Appliance(type='microwave', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=12, building=1, dataset='REDD', appliances=[Appliance(type='unknown', instance=1)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=13, building=1, dataset='REDD', appliances=[Appliance(type='electric space heater', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=14, building=1, dataset='REDD', appliances=[Appliance(type='electric stove', instance=1)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=15, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=3)])'\n",
        "Estimating power demand for 'ElecMeter(instance=16, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=4)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=17, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=2)])'\n",
        "Estimating power demand for 'ElecMeter(instance=18, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=3)])'\n",
        "Estimating power demand for 'ElecMeter(instance=19, building=1, dataset='REDD', appliances=[Appliance(type='unknown', instance=2)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'MeterGroup(meters=\n",
        "  ElecMeter(instance=3, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
        "  ElecMeter(instance=4, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
        ")'\n",
        "Estimating power demand for 'MeterGroup(meters=\n",
        "  ElecMeter(instance=10, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
        "  ElecMeter(instance=20, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
        ")'\n",
        "\r",
        "Loading data for meter"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " ElecMeterID(instance=1, building=1, dataset='REDD')     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "Loading data for meter ElecMeterID(instance=2, building=1, dataset='REDD')     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Done loading data all meters for this chunk.\n",
        "Estimating power demand for 'ElecMeter(instance=5, building=1, dataset='REDD', appliances=[Appliance(type='fridge', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=6, building=1, dataset='REDD', appliances=[Appliance(type='dish washer', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=7, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=1)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=8, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=2)])'\n",
        "Estimating power demand for 'ElecMeter(instance=9, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=1)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=11, building=1, dataset='REDD', appliances=[Appliance(type='microwave', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=12, building=1, dataset='REDD', appliances=[Appliance(type='unknown', instance=1)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=13, building=1, dataset='REDD', appliances=[Appliance(type='electric space heater', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=14, building=1, dataset='REDD', appliances=[Appliance(type='electric stove', instance=1)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=15, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=3)])'\n",
        "Estimating power demand for 'ElecMeter(instance=16, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=4)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=17, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=2)])'\n",
        "Estimating power demand for 'ElecMeter(instance=18, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=3)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=19, building=1, dataset='REDD', appliances=[Appliance(type='unknown', instance=2)])'\n",
        "Estimating power demand for 'MeterGroup(meters=\n",
        "  ElecMeter(instance=3, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
        "  ElecMeter(instance=4, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
        ")'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'MeterGroup(meters=\n",
        "  ElecMeter(instance=10, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
        "  ElecMeter(instance=20, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
        ")'\n",
        "\r",
        "Loading data for meter"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " ElecMeterID(instance=1, building=1, dataset='REDD')     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "Loading data for meter ElecMeterID(instance=2, building=1, dataset='REDD')     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Done loading data all meters for this chunk.\n",
        "Estimating power demand for 'ElecMeter(instance=5, building=1, dataset='REDD', appliances=[Appliance(type='fridge', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=6, building=1, dataset='REDD', appliances=[Appliance(type='dish washer', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=7, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=1)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=8, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=2)])'\n",
        "Estimating power demand for 'ElecMeter(instance=9, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=1)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=11, building=1, dataset='REDD', appliances=[Appliance(type='microwave', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=12, building=1, dataset='REDD', appliances=[Appliance(type='unknown', instance=1)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=13, building=1, dataset='REDD', appliances=[Appliance(type='electric space heater', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=14, building=1, dataset='REDD', appliances=[Appliance(type='electric stove', instance=1)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=15, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=3)])'\n",
        "Estimating power demand for 'ElecMeter(instance=16, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=4)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=17, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=2)])'\n",
        "Estimating power demand for 'ElecMeter(instance=18, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=3)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=19, building=1, dataset='REDD', appliances=[Appliance(type='unknown', instance=2)])'\n",
        "Estimating power demand for 'MeterGroup(meters=\n",
        "  ElecMeter(instance=3, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
        "  ElecMeter(instance=4, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
        ")'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'MeterGroup(meters=\n",
        "  ElecMeter(instance=10, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
        "  ElecMeter(instance=20, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
        ")'\n",
        "\r",
        "Loading data for meter"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " ElecMeterID(instance=1, building=1, dataset='REDD')     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "Loading data for meter ElecMeterID(instance=2, building=1, dataset='REDD')     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Done loading data all meters for this chunk.\n",
        "\r",
        "Loading data for meter ElecMeterID(instance=1, building=1, dataset='REDD')     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "Loading data for meter ElecMeterID(instance=2, building=1, dataset='REDD')     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Done loading data all meters for this chunk.\n",
        "Estimating power demand for 'ElecMeter(instance=5, building=1, dataset='REDD', appliances=[Appliance(type='fridge', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=6, building=1, dataset='REDD', appliances=[Appliance(type='dish washer', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=7, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=1)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=8, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=2)])'\n",
        "Estimating power demand for 'ElecMeter(instance=9, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=1)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=11, building=1, dataset='REDD', appliances=[Appliance(type='microwave', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=12, building=1, dataset='REDD', appliances=[Appliance(type='unknown', instance=1)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=13, building=1, dataset='REDD', appliances=[Appliance(type='electric space heater', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=14, building=1, dataset='REDD', appliances=[Appliance(type='electric stove', instance=1)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=15, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=3)])'\n",
        "Estimating power demand for 'ElecMeter(instance=16, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=4)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=17, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=2)])'\n",
        "Estimating power demand for 'ElecMeter(instance=18, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=3)])'\n",
        "Estimating power demand for 'ElecMeter(instance=19, building=1, dataset='REDD', appliances=[Appliance(type='unknown', instance=2)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'MeterGroup(meters=\n",
        "  ElecMeter(instance=3, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
        "  ElecMeter(instance=4, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
        ")'\n",
        "Estimating power demand for 'MeterGroup(meters=\n",
        "  ElecMeter(instance=10, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
        "  ElecMeter(instance=20, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
        ")'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\r",
        "Loading data for meter"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " ElecMeterID(instance=1, building=1, dataset='REDD')     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "Loading data for meter ElecMeterID(instance=2, building=1, dataset='REDD')     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Done loading data all meters for this chunk.\n",
        "Estimating power demand for 'ElecMeter(instance=5, building=1, dataset='REDD', appliances=[Appliance(type='fridge', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=6, building=1, dataset='REDD', appliances=[Appliance(type='dish washer', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=7, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=8, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=2)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=9, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=11, building=1, dataset='REDD', appliances=[Appliance(type='microwave', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=12, building=1, dataset='REDD', appliances=[Appliance(type='unknown', instance=1)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=13, building=1, dataset='REDD', appliances=[Appliance(type='electric space heater', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=14, building=1, dataset='REDD', appliances=[Appliance(type='electric stove', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=15, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=3)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=16, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=4)])'\n",
        "Estimating power demand for 'ElecMeter(instance=17, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=2)])'\n",
        "Estimating power demand for 'ElecMeter(instance=18, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=3)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=19, building=1, dataset='REDD', appliances=[Appliance(type='unknown', instance=2)])'\n",
        "Estimating power demand for 'MeterGroup(meters=\n",
        "  ElecMeter(instance=3, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
        "  ElecMeter(instance=4, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
        ")'\n",
        "Estimating power demand for 'MeterGroup(meters=\n",
        "  ElecMeter(instance=10, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
        "  ElecMeter(instance=20, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
        ")'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\r",
        "Loading data for meter ElecMeterID(instance=1, building=1, dataset='REDD')     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "Loading data for meter ElecMeterID(instance=2, building=1, dataset='REDD')     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Done loading data all meters for this chunk.\n",
        "Estimating power demand for 'ElecMeter(instance=5, building=1, dataset='REDD', appliances=[Appliance(type='fridge', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=6, building=1, dataset='REDD', appliances=[Appliance(type='dish washer', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=7, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=8, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=2)])'\n",
        "Estimating power demand for 'ElecMeter(instance=9, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=1)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=11, building=1, dataset='REDD', appliances=[Appliance(type='microwave', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=12, building=1, dataset='REDD', appliances=[Appliance(type='unknown', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=13, building=1, dataset='REDD', appliances=[Appliance(type='electric space heater', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=14, building=1, dataset='REDD', appliances=[Appliance(type='electric stove', instance=1)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=15, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=3)])'\n",
        "Estimating power demand for 'ElecMeter(instance=16, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=4)])'\n",
        "Estimating power demand for 'ElecMeter(instance=17, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=2)])'\n",
        "Estimating power demand for 'ElecMeter(instance=18, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=3)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=19, building=1, dataset='REDD', appliances=[Appliance(type='unknown', instance=2)])'\n",
        "Estimating power demand for 'MeterGroup(meters=\n",
        "  ElecMeter(instance=3, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
        "  ElecMeter(instance=4, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
        ")'\n",
        "Estimating power demand for 'MeterGroup(meters=\n",
        "  ElecMeter(instance=10, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
        "  ElecMeter(instance=20, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
        ")'\n",
        "\r",
        "Loading data for meter"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " ElecMeterID(instance=1, building=1, dataset='REDD')     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "Loading data for meter ElecMeterID(instance=2, building=1, dataset='REDD')     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Done loading data all meters for this chunk.\n",
        "Estimating power demand for 'ElecMeter(instance=5, building=1, dataset='REDD', appliances=[Appliance(type='fridge', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=6, building=1, dataset='REDD', appliances=[Appliance(type='dish washer', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=7, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=1)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=8, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=2)])'\n",
        "Estimating power demand for 'ElecMeter(instance=9, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=11, building=1, dataset='REDD', appliances=[Appliance(type='microwave', instance=1)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=12, building=1, dataset='REDD', appliances=[Appliance(type='unknown', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=13, building=1, dataset='REDD', appliances=[Appliance(type='electric space heater', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=14, building=1, dataset='REDD', appliances=[Appliance(type='electric stove', instance=1)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=15, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=3)])'\n",
        "Estimating power demand for 'ElecMeter(instance=16, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=4)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=17, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=2)])'\n",
        "Estimating power demand for 'ElecMeter(instance=18, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=3)])'\n",
        "Estimating power demand for 'ElecMeter(instance=19, building=1, dataset='REDD', appliances=[Appliance(type='unknown', instance=2)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'MeterGroup(meters=\n",
        "  ElecMeter(instance=3, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
        "  ElecMeter(instance=4, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
        ")'\n",
        "Estimating power demand for 'MeterGroup(meters=\n",
        "  ElecMeter(instance=10, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
        "  ElecMeter(instance=20, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
        ")'\n",
        "\r",
        "Loading data for meter"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " ElecMeterID(instance=1, building=1, dataset='REDD')     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "Loading data for meter ElecMeterID(instance=2, building=1, dataset='REDD')     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Done loading data all meters for this chunk.\n",
        "\r",
        "Loading data for meter ElecMeterID(instance=1, building=1, dataset='REDD')     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "Loading data for meter ElecMeterID(instance=2, building=1, dataset='REDD')     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Done loading data all meters for this chunk.\n",
        "Estimating power demand for 'ElecMeter(instance=5, building=1, dataset='REDD', appliances=[Appliance(type='fridge', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=6, building=1, dataset='REDD', appliances=[Appliance(type='dish washer', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=7, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=8, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=2)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=9, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=11, building=1, dataset='REDD', appliances=[Appliance(type='microwave', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=12, building=1, dataset='REDD', appliances=[Appliance(type='unknown', instance=1)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=13, building=1, dataset='REDD', appliances=[Appliance(type='electric space heater', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=14, building=1, dataset='REDD', appliances=[Appliance(type='electric stove', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=15, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=3)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=16, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=4)])'\n",
        "Estimating power demand for 'ElecMeter(instance=17, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=2)])'\n",
        "Estimating power demand for 'ElecMeter(instance=18, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=3)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=19, building=1, dataset='REDD', appliances=[Appliance(type='unknown', instance=2)])'\n",
        "Estimating power demand for 'MeterGroup(meters=\n",
        "  ElecMeter(instance=3, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
        "  ElecMeter(instance=4, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
        ")'\n",
        "Estimating power demand for 'MeterGroup(meters=\n",
        "  ElecMeter(instance=10, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
        "  ElecMeter(instance=20, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
        ")'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\r",
        "Loading data for meter ElecMeterID(instance=1, building=1, dataset='REDD')     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "Loading data for meter ElecMeterID(instance=2, building=1, dataset='REDD')     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Done loading data all meters for this chunk.\n",
        "\r",
        "Loading data for meter ElecMeterID(instance=1, building=1, dataset='REDD')     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "Loading data for meter ElecMeterID(instance=2, building=1, dataset='REDD')     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Done loading data all meters for this chunk.\n",
        "Estimating power demand for 'ElecMeter(instance=5, building=1, dataset='REDD', appliances=[Appliance(type='fridge', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=6, building=1, dataset='REDD', appliances=[Appliance(type='dish washer', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=7, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=1)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=8, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=2)])'\n",
        "Estimating power demand for 'ElecMeter(instance=9, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=11, building=1, dataset='REDD', appliances=[Appliance(type='microwave', instance=1)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=12, building=1, dataset='REDD', appliances=[Appliance(type='unknown', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=13, building=1, dataset='REDD', appliances=[Appliance(type='electric space heater', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=14, building=1, dataset='REDD', appliances=[Appliance(type='electric stove', instance=1)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=15, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=3)])'\n",
        "Estimating power demand for 'ElecMeter(instance=16, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=4)])'\n",
        "Estimating power demand for 'ElecMeter(instance=17, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=2)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=18, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=3)])'\n",
        "Estimating power demand for 'ElecMeter(instance=19, building=1, dataset='REDD', appliances=[Appliance(type='unknown', instance=2)])'\n",
        "Estimating power demand for 'MeterGroup(meters=\n",
        "  ElecMeter(instance=3, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
        "  ElecMeter(instance=4, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
        ")'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'MeterGroup(meters=\n",
        "  ElecMeter(instance=10, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
        "  ElecMeter(instance=20, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
        ")'\n",
        "\r",
        "Loading data for meter"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " ElecMeterID(instance=1, building=1, dataset='REDD')     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "Loading data for meter ElecMeterID(instance=2, building=1, dataset='REDD')     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Done loading data all meters for this chunk.\n",
        "Estimating power demand for 'ElecMeter(instance=5, building=1, dataset='REDD', appliances=[Appliance(type='fridge', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=6, building=1, dataset='REDD', appliances=[Appliance(type='dish washer', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=7, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=8, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=2)])'\n",
        "Estimating power demand for 'ElecMeter(instance=9, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=1)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=11, building=1, dataset='REDD', appliances=[Appliance(type='microwave', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=12, building=1, dataset='REDD', appliances=[Appliance(type='unknown', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=13, building=1, dataset='REDD', appliances=[Appliance(type='electric space heater', instance=1)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=14, building=1, dataset='REDD', appliances=[Appliance(type='electric stove', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=15, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=3)])'\n",
        "Estimating power demand for 'ElecMeter(instance=16, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=4)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=17, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=2)])'\n",
        "Estimating power demand for 'ElecMeter(instance=18, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=3)])'\n",
        "Estimating power demand for 'ElecMeter(instance=19, building=1, dataset='REDD', appliances=[Appliance(type='unknown', instance=2)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'MeterGroup(meters=\n",
        "  ElecMeter(instance=3, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
        "  ElecMeter(instance=4, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
        ")'\n",
        "Estimating power demand for 'MeterGroup(meters=\n",
        "  ElecMeter(instance=10, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
        "  ElecMeter(instance=20, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
        ")'\n",
        "\r",
        "Loading data for meter"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " ElecMeterID(instance=1, building=1, dataset='REDD')     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "Loading data for meter ElecMeterID(instance=2, building=1, dataset='REDD')     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Done loading data all meters for this chunk.\n",
        "Estimating power demand for 'ElecMeter(instance=5, building=1, dataset='REDD', appliances=[Appliance(type='fridge', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=6, building=1, dataset='REDD', appliances=[Appliance(type='dish washer', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=7, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=8, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=2)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=9, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=11, building=1, dataset='REDD', appliances=[Appliance(type='microwave', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=12, building=1, dataset='REDD', appliances=[Appliance(type='unknown', instance=1)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=13, building=1, dataset='REDD', appliances=[Appliance(type='electric space heater', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=14, building=1, dataset='REDD', appliances=[Appliance(type='electric stove', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=15, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=3)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=16, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=4)])'\n",
        "Estimating power demand for 'ElecMeter(instance=17, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=2)])'\n",
        "Estimating power demand for 'ElecMeter(instance=18, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=3)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=19, building=1, dataset='REDD', appliances=[Appliance(type='unknown', instance=2)])'\n",
        "Estimating power demand for 'MeterGroup(meters=\n",
        "  ElecMeter(instance=3, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
        "  ElecMeter(instance=4, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
        ")'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'MeterGroup(meters=\n",
        "  ElecMeter(instance=10, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
        "  ElecMeter(instance=20, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
        ")'\n",
        "\r",
        "Loading data for meter"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " ElecMeterID(instance=1, building=1, dataset='REDD')     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "Loading data for meter ElecMeterID(instance=2, building=1, dataset='REDD')     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Done loading data all meters for this chunk.\n",
        "Estimating power demand for 'ElecMeter(instance=5, building=1, dataset='REDD', appliances=[Appliance(type='fridge', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=6, building=1, dataset='REDD', appliances=[Appliance(type='dish washer', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=7, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=1)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=8, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=2)])'\n",
        "Estimating power demand for 'ElecMeter(instance=9, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=11, building=1, dataset='REDD', appliances=[Appliance(type='microwave', instance=1)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=12, building=1, dataset='REDD', appliances=[Appliance(type='unknown', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=13, building=1, dataset='REDD', appliances=[Appliance(type='electric space heater', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=14, building=1, dataset='REDD', appliances=[Appliance(type='electric stove', instance=1)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=15, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=3)])'\n",
        "Estimating power demand for 'ElecMeter(instance=16, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=4)])'\n",
        "Estimating power demand for 'ElecMeter(instance=17, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=2)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=18, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=3)])'\n",
        "Estimating power demand for 'ElecMeter(instance=19, building=1, dataset='REDD', appliances=[Appliance(type='unknown', instance=2)])'\n",
        "Estimating power demand for 'MeterGroup(meters=\n",
        "  ElecMeter(instance=3, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
        "  ElecMeter(instance=4, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
        ")'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'MeterGroup(meters=\n",
        "  ElecMeter(instance=10, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
        "  ElecMeter(instance=20, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
        ")'\n",
        "\r",
        "Loading data for meter"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " ElecMeterID(instance=1, building=1, dataset='REDD')     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "Loading data for meter ElecMeterID(instance=2, building=1, dataset='REDD')     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Done loading data all meters for this chunk.\n",
        "Estimating power demand for 'ElecMeter(instance=5, building=1, dataset='REDD', appliances=[Appliance(type='fridge', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=6, building=1, dataset='REDD', appliances=[Appliance(type='dish washer', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=7, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=8, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=2)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=9, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=11, building=1, dataset='REDD', appliances=[Appliance(type='microwave', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=12, building=1, dataset='REDD', appliances=[Appliance(type='unknown', instance=1)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=13, building=1, dataset='REDD', appliances=[Appliance(type='electric space heater', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=14, building=1, dataset='REDD', appliances=[Appliance(type='electric stove', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=15, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=3)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=16, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=4)])'\n",
        "Estimating power demand for 'ElecMeter(instance=17, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=2)])'\n",
        "Estimating power demand for 'ElecMeter(instance=18, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=3)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=19, building=1, dataset='REDD', appliances=[Appliance(type='unknown', instance=2)])'\n",
        "Estimating power demand for 'MeterGroup(meters=\n",
        "  ElecMeter(instance=3, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
        "  ElecMeter(instance=4, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
        ")'\n",
        "Estimating power demand for 'MeterGroup(meters=\n",
        "  ElecMeter(instance=10, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
        "  ElecMeter(instance=20, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
        ")'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\r",
        "Loading data for meter ElecMeterID(instance=1, building=1, dataset='REDD')     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "Loading data for meter ElecMeterID(instance=2, building=1, dataset='REDD')     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Done loading data all meters for this chunk.\n",
        "Estimating power demand for 'ElecMeter(instance=5, building=1, dataset='REDD', appliances=[Appliance(type='fridge', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=6, building=1, dataset='REDD', appliances=[Appliance(type='dish washer', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=7, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=8, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=2)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=9, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=11, building=1, dataset='REDD', appliances=[Appliance(type='microwave', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=12, building=1, dataset='REDD', appliances=[Appliance(type='unknown', instance=1)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=13, building=1, dataset='REDD', appliances=[Appliance(type='electric space heater', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=14, building=1, dataset='REDD', appliances=[Appliance(type='electric stove', instance=1)])'\n",
        "Estimating power demand for 'ElecMeter(instance=15, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=3)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=16, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=4)])'\n",
        "Estimating power demand for 'ElecMeter(instance=17, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=2)])'\n",
        "Estimating power demand for 'ElecMeter(instance=18, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=3)])'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimating power demand for 'ElecMeter(instance=19, building=1, dataset='REDD', appliances=[Appliance(type='unknown', instance=2)])'\n",
        "Estimating power demand for 'MeterGroup(meters=\n",
        "  ElecMeter(instance=3, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
        "  ElecMeter(instance=4, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
        ")'\n",
        "Estimating power demand for 'MeterGroup(meters=\n",
        "  ElecMeter(instance=10, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
        "  ElecMeter(instance=20, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
        ")'"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\r",
        "Loading data for meter ElecMeterID(instance=1, building=1, dataset='REDD')     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "Loading data for meter ElecMeterID(instance=2, building=1, dataset='REDD')     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Done loading data all meters for this chunk.\n"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Examine disaggregated data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "disag = DataSet(disag_filename)\n",
      "disag_elec = disag.buildings[building_number].elec\n",
      "disag_elec.plot()\n",
      "disag.store.close()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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q1XTv3p2AgACCg4O55ZZb+Prrryt8PmMMPw8ALT8lxM5UpypPbq3NByKNMU2A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\nThEg+KHIvjK+WsdWQsU7YxupQFNDkaUhFNiKSCzp0KED+/fvr7Fv796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UqVNxOBykpKQw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\naZqm1Wr/DwJJVpaV0M0EAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10b25fd50>"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Calculate accuracy of disaggregation"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "disag = DataSet(disag_filename)\n",
      "disag_elec = disag.buildings[building_number].elec\n",
      "\n",
      "f1 = f1_score(disag_elec, elec)\n",
      "f1.index = disag_elec.get_labels(f1.index)\n",
      "f1.plot(kind='bar')\n",
      "plt.xlabel('appliance');\n",
      "plt.ylabel('f-score');\n",
      "\n",
      "disag.store.close()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "ValueError",
       "evalue": "Unknown format code 'd' for object of type 'str'",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
        "\u001b[0;32m<ipython-input-27-7c2441b23c13>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mdisag_elec\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdisag\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuildings\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mbuilding_number\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0melec\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mf1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf1_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdisag_elec\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0melec\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0mf1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdisag_elec\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_labels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mf1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'bar'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/nipunbatra/git/nilmtk/nilmtk/metrics.pyc\u001b[0m in \u001b[0;36mf1_score\u001b[0;34m(predictions, ground_truth)\u001b[0m\n\u001b[1;32m    220\u001b[0m         for aligned_states_chunk in align_two_meters(pred_meter, \n\u001b[1;32m    221\u001b[0m                                                      \u001b[0mground_truth_meter\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 222\u001b[0;31m                                                      'when_on'):\n\u001b[0m\u001b[1;32m    223\u001b[0m             \u001b[0maligned_states_chunk\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdropna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    224\u001b[0m             \u001b[0maligned_states_chunk\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maligned_states_chunk\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/nipunbatra/git/nilmtk/nilmtk/electric.py\u001b[0m in \u001b[0;36malign_two_meters\u001b[0;34m(master, slave, func)\u001b[0m\n\u001b[1;32m    757\u001b[0m     \u001b[0msample_period\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmaster\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msample_period\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    758\u001b[0m     \u001b[0mperiod_alias\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'{:d}S'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msample_period\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 759\u001b[0;31m     \u001b[0msections\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmaster\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgood_sections\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    760\u001b[0m     \u001b[0mmaster_generator\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmaster\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msections\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msections\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    761\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mmaster_chunk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmaster_generator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/nipunbatra/git/nilmtk/nilmtk/elecmeter.py\u001b[0m in \u001b[0;36mgood_sections\u001b[0;34m(self, **loader_kwargs)\u001b[0m\n\u001b[1;32m    633\u001b[0m         \u001b[0mresults_obj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGoodSections\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresults_class\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'max_sample_period'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    634\u001b[0m         return self._get_stat_from_cache_or_compute(\n\u001b[0;32m--> 635\u001b[0;31m             nodes, results_obj, loader_kwargs)        \n\u001b[0m\u001b[1;32m    636\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    637\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_stat_from_cache_or_compute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnodes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresults_obj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloader_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/nipunbatra/git/nilmtk/nilmtk/elecmeter.py\u001b[0m in \u001b[0;36m_get_stat_from_cache_or_compute\u001b[0;34m(self, nodes, results_obj, loader_kwargs)\u001b[0m\n\u001b[1;32m    683\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    684\u001b[0m         \u001b[0;31m# Retrieve usable stats from cache\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 685\u001b[0;31m         \u001b[0mkey_for_cached_stat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkey_for_cached_stat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresults_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    686\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mloader_kwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'preprocessing'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    687\u001b[0m             \u001b[0mcached_stat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_cached_stat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey_for_cached_stat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/nipunbatra/git/nilmtk/nilmtk/elecmeter.py\u001b[0m in \u001b[0;36mkey_for_cached_stat\u001b[0;34m(self, stat_name)\u001b[0m\n\u001b[1;32m    784\u001b[0m         \"\"\"\n\u001b[1;32m    785\u001b[0m         return (\"building{:d}/elec/cache/meter{:d}/{:s}\"\n\u001b[0;32m--> 786\u001b[0;31m                 .format(self.building(), self.instance(), stat_name))\n\u001b[0m\u001b[1;32m    787\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    788\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclear_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;31mValueError\u001b[0m: Unknown format code 'd' for object of type 'str'"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "elec.clear_cache()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Removed building1/elec/cache/meter1/\n",
        "Removed building1/elec/cache/meter2/\n",
        "Removed building1/elec/cache/meter5/\n"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "disag_elec.clear_cache()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Removed building1/elec/cache/meter5/\n",
        "Removed building1/elec/cache/meter6/\n",
        "Removed building1/elec/cache/meter7/\n",
        "Removed building1/elec/cache/meter8/\n",
        "Removed building1/elec/cache/meter9/\n",
        "Removed building1/elec/cache/meter11/\n",
        "Removed building1/elec/cache/meter12/\n",
        "Removed building1/elec/cache/meter13/\n",
        "Removed building1/elec/cache/meter14/\n",
        "Removed building1/elec/cache/meter15/\n",
        "Removed building1/elec/cache/meter16/\n",
        "Removed building1/elec/cache/meter17/\n",
        "Removed building1/elec/cache/meter18/\n",
        "Removed building1/elec/cache/meter19/\n"
       ]
      },
      {
       "ename": "ValueError",
       "evalue": "Unknown format code 'd' for object of type 'str'",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
        "\u001b[0;32m<ipython-input-26-54f9aaa831ee>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdisag_elec\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclear_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[0;32m/Users/nipunbatra/git/nilmtk/nilmtk/metergroup.py\u001b[0m in \u001b[0;36mclear_cache\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1601\u001b[0m         \u001b[0;34m\"\"\"Clear cache on all meters in this MeterGroup.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1602\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mmeter\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmeters\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1603\u001b[0;31m             \u001b[0mmeter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclear_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1604\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1605\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mcorrelation_of_sum_of_submeters_with_mains\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mload_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/nipunbatra/git/nilmtk/nilmtk/elecmeter.py\u001b[0m in \u001b[0;36mclear_cache\u001b[0;34m(self, verbose)\u001b[0m\n\u001b[1;32m    796\u001b[0m         \"\"\"\n\u001b[1;32m    797\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstore\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 798\u001b[0;31m             \u001b[0mkey_for_cache\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkey_for_cached_stat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m''\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    799\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    800\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey_for_cache\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/nipunbatra/git/nilmtk/nilmtk/elecmeter.py\u001b[0m in \u001b[0;36mkey_for_cached_stat\u001b[0;34m(self, stat_name)\u001b[0m\n\u001b[1;32m    784\u001b[0m         \"\"\"\n\u001b[1;32m    785\u001b[0m         return (\"building{:d}/elec/cache/meter{:d}/{:s}\"\n\u001b[0;32m--> 786\u001b[0;31m                 .format(self.building(), self.instance(), stat_name))\n\u001b[0m\u001b[1;32m    787\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    788\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclear_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;31mValueError\u001b[0m: Unknown format code 'd' for object of type 'str'"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}